In automatic cancer screening, a sample containing cells taken from a human subject and transferred on a slide is imaged and the resultant cytological image is analyzed by a computer to search for any cancerous cell or precancerous abnormality. In the art, a CNN is often used to classify the cells for identifying possible cancerous cells or precancerous abnormalities due to the high classification accuracy achieved by the CNN, e.g., as used in a system disclosed in a pending U.S. patent application Ser. No. 15/910,131 filed Mar. 2, 2018, the disclosure of which is incorporated by reference herein. The CNN comprises plural layers for generating feature maps or heatmaps from a testing image. The last layer of the CNN is a classifying layer. Apart from the classifying layer, each of the remaining layers in the CNN may be a convolutional layer, a subsampling layer or a pooling layer. The classifying layer may be a fully connected layer or a convolutional layer. If the classifying layer is a convolutional layer, the CNN becomes a fully convolutional neural network or a FCN in short. Since computing a sequence of convolutional products for an image has a high degree of parallelism, specialized processors such as GPUs have been designed for exploiting this parallelism to speed up convolution computation for the image. The FCN can be implemented by a specialized processor with an optimized hardware configuration for speeding up cell classification. Advantageously, automatic cancer screening using the FCN for cell classification can be performed faster than using a CNN that employs a fully connected layer as a classifying layer. Despite a speed-up in cell classification is obtained by using the FCN, it is desirable if further speed-up can be achieved.
Usually, a typical testing image containing cells to be classified is sparse so that a large percentage of image area is often a background not contributory to cancer screening. However, the hardware configuration of the specialized processor is usually optimized to continuously compute convolutional products based on a sliding window-based scanning approach. A high percentage of computational effort may be wasted due to the sparsity of the cells in the testing image. A skipping methodology may be used, aiming at computing convolutional products for an identified plurality of ROIs each containing one or more cells clustered together while skipping convolution computation for the background. However, a significant drawback of using the skipping methodology is that jumping from one ROI to another in convolution computation destroys the inherent parallelism present in continuously computing convolutional products across an input image. The specialized processor, having an optimized hardware configuration for implementing the sliding-window scanning approach results in a low computation efficiency in executing the FCN for cell classification with the skipping methodology. Re-optimizing the hardware configuration to take into account the presence of skipping is very difficult if not impossible.
There is a need in the art for a technique to further speed up cell classification by using a FCN without a need to re-optimize the hardware configuration of the specialized processor.